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提交 f06f537a authored 作者: kvmanohar22's avatar kvmanohar22
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modified numpy imports to one common form

上级 1db72747
隐藏空白字符变更
内嵌 并排
正在显示 包含 37 行增加 和 37 行删除
theano/sparse/basic.py
浏览文件 @ f06f537a
......@@ -12,7 +12,7 @@ from __future__ import absolute_import, print_function, division
import sys
import numpy
import numpy as np
from numpy.lib.stride_tricks import as_strided
from six import integer_types
from six.moves import xrange
......@@ -86,11 +86,11 @@ def _is_dense(x):
L{numpy.ndarray}).
"""
if not isinstance(x, (scipy.sparse.spmatrix, numpy.ndarray)):
if not isinstance(x, (scipy.sparse.spmatrix, np.ndarray)):
raise NotImplementedError("this function should only be called on "
"sparse.scipy.sparse.spmatrix or "
"numpy.ndarray, not,", x)
return isinstance(x, numpy.ndarray)
return isinstance(x, np.ndarray)
# Wrapper type
......@@ -205,8 +205,8 @@ def sp_zeros_like(x):
# TODO: don't restrict to CSM formats
_, _, indptr, shape = csm_properties(x)
return CSM(format=x.format)(data=numpy.array([], dtype=x.type.dtype),
indices=numpy.array([], dtype='int32'),
return CSM(format=x.format)(data=np.array([], dtype=x.type.dtype),
indices=np.array([], dtype='int32'),
indptr=tensor.zeros_like(indptr),
shape=shape)
......@@ -293,9 +293,9 @@ class _sparse_py_operators:
args = args,
if len(args) == 2:
scalar_arg_1 = (numpy.isscalar(args[0]) or
scalar_arg_1 = (np.isscalar(args[0]) or
getattr(args[0], 'type', None) == tensor.iscalar)
scalar_arg_2 = (numpy.isscalar(args[1]) or
scalar_arg_2 = (np.isscalar(args[1]) or
getattr(args[1], 'type', None) == tensor.iscalar)
if scalar_arg_1 and scalar_arg_2:
ret = get_item_scalar(self, args)
......@@ -554,17 +554,17 @@ class CSM(gof.Op):
data = tensor.as_tensor_variable(data)
if not isinstance(indices, gof.Variable):
indices_ = numpy.asarray(indices)
indices_ = np.asarray(indices)
indices_32 = theano._asarray(indices, dtype='int32')
assert (indices_ == indices_32).all()
indices = indices_32
if not isinstance(indptr, gof.Variable):
indptr_ = numpy.asarray(indptr)
indptr_ = np.asarray(indptr)
indptr_32 = theano._asarray(indptr, dtype='int32')
assert (indptr_ == indptr_32).all()
indptr = indptr_32
if not isinstance(shape, gof.Variable):
shape_ = numpy.asarray(shape)
shape_ = np.asarray(shape)
shape_32 = theano._asarray(shape, dtype='int32')
assert (shape_ == shape_32).all()
shape = shape_32
......@@ -606,7 +606,7 @@ class CSM(gof.Op):
if self.format == 'csc':
out[0] = scipy.sparse.csc_matrix((data, indices.copy(),
indptr.copy()),
numpy.asarray(shape), copy=False)
np.asarray(shape), copy=False)
else:
assert self.format == 'csr'
out[0] = scipy.sparse.csr_matrix((data, indices.copy(),
......@@ -729,8 +729,8 @@ class CSMGrad(gof.op.Op):
else:
sp_dim = x_shape[0]
g_row = numpy.zeros(sp_dim, dtype=g_data.dtype)
gout_data = numpy.zeros(x_data.shape, dtype=node.outputs[0].dtype)
g_row = np.zeros(sp_dim, dtype=g_data.dtype)
gout_data = np.zeros(x_data.shape, dtype=node.outputs[0].dtype)
for i in range(len(x_indptr) - 1):
for j_ptr in range(g_indptr[i], g_indptr[i + 1]):
......@@ -1100,7 +1100,7 @@ class GetItem2Lists(gof.op.Op):
x = inp[0]
ind1 = inp[1]
ind2 = inp[2]
out[0] = numpy.asarray(x[ind1, ind2]).flatten()
out[0] = np.asarray(x[ind1, ind2]).flatten()
"""
Here scipy returns the corresponding elements in a matrix which isn't
what we are aiming for. Using asarray and flatten, out[0] becomes an
......@@ -1244,7 +1244,7 @@ class GetItem2d(gof.op.Op):
elif ((isinstance(ind, gof.Variable) and
getattr(ind, 'ndim', -1) == 0) or
numpy.isscalar(ind)):
np.isscalar(ind)):
raise NotImplementedError(
'Theano has no sparse vector' +
'Use X[a:b, c:d], X[a:b, c:c+1] or X[a:b] instead.')
......@@ -1653,9 +1653,9 @@ class SpSum(gof.op.Op):
(x,) = inputs
(z,) = outputs
if self.axis is None:
z[0] = numpy.asarray(x.sum())
z[0] = np.asarray(x.sum())
else:
z[0] = numpy.asarray(x.sum(self.axis)).ravel()
z[0] = np.asarray(x.sum(self.axis)).ravel()
def grad(self, inputs, gout):
(x,) = inputs
......@@ -2540,7 +2540,7 @@ class __ComparisonOpSD(gof.op.Op):
assert x.shape == y.shape
assert _is_dense(y)
o = self.comparison(x, y).astype('uint8')
o = numpy.asarray(o)
o = np.asarray(o)
out[0] = o
def infer_shape(self, node, ins_shapes):
......@@ -3382,7 +3382,7 @@ class TrueDot(gof.op.Op):
# 'ushort', 'intc', 'uintc', 'longlong', 'ulonglong', 'single',
# 'double', 'longdouble', 'csingle', 'cdouble', 'clongdouble']
# But ulonglong is uint64 on x86-64, but with a different typenum!
if rval.dtype.num != numpy.dtype(str(rval.dtype)).num:
if rval.dtype.num != np.dtype(str(rval.dtype)).num:
assert str(rval.dtype) == node.outputs[0].dtype
# Create a view with the expected typenum.
format = node.outputs[0].type.format
......@@ -3509,7 +3509,7 @@ class StructuredDot(gof.Op):
# dot of an NxM sparse matrix, with a Mx1 dense matrix, returns vector
# not matrix
if variable.ndim == 1:
variable = numpy.expand_dims(variable, 1)
variable = np.expand_dims(variable, 1)
elif variable.ndim != 2:
raise Exception('Output of structured dot should be a matrix '
'(ndim=2)')
......@@ -3622,7 +3622,7 @@ class StructuredDotGradCSC(gof.Op):
def perform(self, node, inputs, outputs):
(a_indices, a_indptr, b, g_ab) = inputs
(out,) = outputs
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
g_a_data = np.zeros(a_indices.shape, dtype=g_ab.dtype)
for j in xrange(len(a_indptr) - 1):
ind0 = a_indptr[j]
ind1 = a_indptr[j + 1]
......@@ -3631,7 +3631,7 @@ class StructuredDotGradCSC(gof.Op):
# Depending on the type of g_ab and b (sparse or dense),
# the following dot product can result in a scalar or
# a (1, 1) sparse matrix.
dot_val = numpy.dot(g_ab[i], b[j].T)
dot_val = np.dot(g_ab[i], b[j].T)
if isinstance(dot_val, scipy.sparse.spmatrix):
dot_val = dot_val[0, 0]
g_a_data[i_idx] = dot_val
......@@ -3752,7 +3752,7 @@ class StructuredDotGradCSR(gof.Op):
def perform(self, node, inputs, outputs):
(a_indices, a_indptr, b, g_ab) = inputs
(out,) = outputs
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
g_a_data = np.zeros(a_indices.shape, dtype=g_ab.dtype)
for i in xrange(len(a_indptr) - 1): # loop over rows
ind0 = a_indptr[i]
ind1 = a_indptr[i + 1]
......@@ -3763,7 +3763,7 @@ class StructuredDotGradCSR(gof.Op):
# Depending on the type of g_ab and b (sparse or dense),
# the following dot product can result in a scalar or
# a (1, 1) sparse matrix.
dot_val = numpy.dot(g_ab[i], b[j].T)
dot_val = np.dot(g_ab[i], b[j].T)
if isinstance(dot_val, scipy.sparse.spmatrix):
dot_val = dot_val[0, 0]
g_a_data[j_idx] = dot_val
......@@ -3910,7 +3910,7 @@ class SamplingDot(gof.op.Op):
if not _is_sparse(p):
raise TypeError(p)
out[0] = p.__class__(p.multiply(numpy.dot(x, y.T)))
out[0] = p.__class__(p.multiply(np.dot(x, y.T)))
def grad(self, inputs, gout):
(x, y, p) = inputs
......@@ -4243,7 +4243,7 @@ class ConstructSparseFromList(gof.Op):
out, = out_
rows, cols = values.shape
assert rows == len(ilist)
indptr = numpy.arange(cols + 1) * rows
indptr = np.arange(cols + 1) * rows
indices = as_strided(ilist,
strides=(0, ilist.strides[0]),
shape=(cols, ilist.shape[0])).flatten()
......
theano/sparse/opt.py
浏览文件 @ f06f537a
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import scipy
import theano
......@@ -879,7 +879,7 @@ local_usmm = gof.opt.PatternSub(
(theano.tensor.sub, 'z',
(theano.tensor.mul,
{'pattern': 'alpha',
'constraint': lambda expr: (numpy.all(expr.type.broadcastable) and
'constraint': lambda expr: (np.all(expr.type.broadcastable) and
theano.config.blas.ldflags)},
(sparse._dot, 'x', 'y'))),
(usmm, (theano.tensor.neg, 'alpha'), 'x', 'y', 'z'))
......
theano/sparse/type.py
浏览文件 @ f06f537a
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
try:
import scipy.sparse
imported_scipy = True
......@@ -20,7 +20,7 @@ def _is_sparse(x):
True iff x is a L{scipy.sparse.spmatrix} (and not a L{numpy.ndarray}).
"""
if not isinstance(x, (scipy.sparse.spmatrix, numpy.ndarray, tuple, list)):
if not isinstance(x, (scipy.sparse.spmatrix, np.ndarray, tuple, list)):
raise NotImplementedError("this function should only be called on "
"sparse.scipy.sparse.spmatrix or "
"numpy.ndarray, not,", x)
......@@ -107,12 +107,12 @@ class SparseType(gof.Type):
return (SparseType.may_share_memory(a, b.data) or
SparseType.may_share_memory(a, b.indices) or
SparseType.may_share_memory(a, b.indptr))
if _is_sparse(b) and isinstance(a, numpy.ndarray):
if _is_sparse(b) and isinstance(a, np.ndarray):
a, b = b, a
if _is_sparse(a) and isinstance(b, numpy.ndarray):
if (numpy.may_share_memory(a.data, b) or
numpy.may_share_memory(a.indices, b) or
numpy.may_share_memory(a.indptr, b)):
if _is_sparse(a) and isinstance(b, np.ndarray):
if (np.may_share_memory(a.data, b) or
np.may_share_memory(a.indices, b) or
np.may_share_memory(a.indptr, b)):
# currently we can't share memory with a.shape as it is a tuple
return True
return False
......@@ -168,8 +168,8 @@ class SparseType(gof.Type):
obj.indices.size, obj.indptr.size, obj.nnz)
def get_size(self, shape_info):
return (shape_info[1] * numpy.dtype(self.dtype).itemsize +
(shape_info[2] + shape_info[3]) * numpy.dtype('int32').itemsize)
return (shape_info[1] * np.dtype(self.dtype).itemsize +
(shape_info[2] + shape_info[3]) * np.dtype('int32').itemsize)
# Register SparseType's C code for ViewOp.
theano.compile.register_view_op_c_code(
......
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